Triple
T1188000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chinese yuan |
E25290
|
entity |
| Predicate | usedAsTradingCurrency |
P518
|
FINISHED |
| Object | true |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: true | Statement: [Chinese yuan, usedAsTradingCurrency, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedAsTradingCurrency Context triple: [Chinese yuan, usedAsTradingCurrency, true]
-
A.
isMajorTradingCurrencyIn
Indicates that a currency is one of the primary or most significant currencies used for trade within a specified market or region.
-
B.
isWorldTradeCurrency
chosen
Indicates that a currency is officially used and widely accepted as a medium of exchange in international trade and global financial transactions.
-
C.
usesCurrency
Indicates that one entity conducts its financial transactions or values using the monetary unit represented by the other entity.
-
D.
isMostTradedCurrency
Indicates that a currency is the one with the highest trading volume or frequency in a given market or context.
-
E.
hasCurrencyUsedInManyCountries
Indicates that a currency is officially used as legal tender in multiple countries.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a49427d98881908646d6c63b8cea1e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd568cf481908d10cf19a3ce28f3 |
completed | March 1, 2026, 10:27 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5bacc481909e8dfd5215e4711a |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:45 p.m.